In the context of Empirical Risk Minimization, which of the following scenarios is most likely to lead to underfitting while impacting the generalization error negatively?

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Seekh · Accepted Answer

Empirical Risk Minimization chooses a hypothesis that minimizes training error, but if the hypothesis class is too simple—such as a linear model for data that follows a curved pattern—then the model will be unable to capture the underlying relationships, producing high bias. Because the model cannot fit the training data well, its training error remains large and its generalization error also stays high. This situation is classic underfitting, where the model is too restrictive and fails to learn the true pattern. For example, fitting a straight line to points that actually follow a quadratic curve will leave large residuals both on the training set and on new data.

In the context of Empirical Risk Minimization, which of the following scenarios is most likely to lead to underfitting while impacting the generalization error negatively?

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Practice Similar Questions

In the context of Empirical Risk Minimization, how does overfitting relate to the choice of loss function?

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Which of the following scenarios best exemplifies the application of Empirical Risk Minimization in model training?

In the context of Empirical Risk Minimization, which factor most directly influences the selection of model parameters?

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